{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 产生分隔的高斯分布的聚类簇 样本分布见 6-1\n",
    "# centers 核心点的数组\n",
    "# num 样本数\n",
    "# std 每簇的标准差\n",
    "def create_data(centers,num=1000,std=0.7):\n",
    "    x, labels_true = make_blobs(n_samples=num, centers=centers, cluster_std=std)\n",
    "    return x, labels_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_Kmeans(*data):\n",
    "    x,labels_true = data\n",
    "    km = cluster.KMeans()\n",
    "    km.fit(x)\n",
    "    predicted_labels = km.predict(x)\n",
    "    print('ARI:{0}'.format(adjusted_rand_score(labels_true,predicted_labels))) #ARI 可以理解为聚类效果 越大越好\n",
    "    print('Sum center distance {0}'.format(km.inertia_)) # 每个样本距离中心的距离的和 越小越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARI:0.38523150119465516\n",
      "Sum center distance 224.99523825333654\n"
     ]
    }
   ],
   "source": [
    "x, labels_true = create_data(((1,1),(2,2),(1,2),(10,20)),1000,0.5)\n",
    "test_Kmeans(x, labels_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 簇数的影响\n",
    "def test_Kmeans_k(*data):\n",
    "    x,labels_true = data\n",
    "    nums = range(1,15)\n",
    "    ARIs =[]\n",
    "    Distances = []\n",
    "    for num in nums:\n",
    "        km = cluster.KMeans(n_clusters=num)\n",
    "        km.fit(x)\n",
    "        predicted_labels = km.predict(x)\n",
    "        ARIs.append(adjusted_rand_score(labels_true,predicted_labels))\n",
    "        Distances.append(km.inertia_)\n",
    "        \n",
    "    # 绘图\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(10)\n",
    "    fig.set_figwidth(30)\n",
    "    ax = fig.add_subplot(121)\n",
    "    ax.plot(nums, ARIs, marker='+')\n",
    "    ax.set_xlabel('n_clusters')\n",
    "    ax.set_ylabel('ARI')\n",
    "    ax = fig.add_subplot(122)\n",
    "    ax.plot(nums, Distances, marker='o')\n",
    "    ax.set_xlabel('n_clusters')\n",
    "    ax.set_ylabel('inertia_')\n",
    "    fig.suptitle('KMeans')\n",
    "    plt.show()\n",
    "\n",
    "x, labels_true = create_data(((1,1),(2,2),(1,2),(10,20)),1000,0.5)\n",
    "test_Kmeans_k(x, labels_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 运行次数 以及 初始中心向量策略的影响\n",
    "def test_Kmeans_n_init(*data):\n",
    "    x,labels_true = data\n",
    "    nums = range(1,20)\n",
    "    # 绘图\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(10)\n",
    "    fig.set_figwidth(25)\n",
    "    \n",
    "    ARIs_k =[]\n",
    "    Distances_k = []\n",
    "    \n",
    "    ARIs_r =[]\n",
    "    Distances_r = []\n",
    "    for num in nums:\n",
    "        km = cluster.KMeans(n_clusters=4, n_init=num, init='k-means++')\n",
    "        km.fit(x)\n",
    "        predicted_labels = km.predict(x)\n",
    "        ARIs_k.append(adjusted_rand_score(labels_true,predicted_labels))\n",
    "        Distances_k.append(km.inertia_)\n",
    "        \n",
    "        km = cluster.KMeans(n_clusters=4, n_init=num, init='random')\n",
    "        km.fit(x)\n",
    "        predicted_labels = km.predict(x)\n",
    "        ARIs_r.append(adjusted_rand_score(labels_true,predicted_labels))\n",
    "        Distances_r.append(km.inertia_)\n",
    "        \n",
    "\n",
    "    ax = fig.add_subplot(121)\n",
    "    ax.plot(nums, ARIs_k, marker='+', label='k-means++')\n",
    "    ax.plot(nums, ARIs_r, marker='+', label='random')\n",
    "    ax.set_xlabel('n_init')\n",
    "    ax.set_ylabel('ARI')\n",
    "    ax.set_ylim(0.4,0.8)\n",
    "    ax.legend(loc='best')\n",
    "    ax = fig.add_subplot(122)\n",
    "    ax.plot(nums, Distances_k, marker='o', label='k-means++')\n",
    "    ax.plot(nums, Distances_r, marker='o', label='random')\n",
    "    ax.set_xlabel('n_init')\n",
    "    ax.set_ylabel('inertia_')\n",
    "    ax.legend(loc='best')\n",
    "    fig.suptitle('KMeans')\n",
    "    plt.show()\n",
    "\n",
    "x, labels_true = create_data(((1,1),(2,2),(1,2),(10,20)),1000,0.5)\n",
    "test_Kmeans_n_init(x, labels_true)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
